library(tidyverse)
library(DT)
library(lubridate)
Load the data into R.
Descriptive Analysis
2-1.
2-2.
2-3.
and
more..
Determine the optimal number of clusters using methods like the Elbow method.
Perform K-means clustering.
Analyze the resulting clusters to interpret different groupings of orders based on acknowledgment times and other relevant factors.
Load the data into R.
I’ve loaded already. Let’s take a look at the dataset to get a sense of what we’re working with.
order_late %>%
dplyr::mutate(order_date = as.Date(order_date),
delivery_date = as.Date(delivery_date),
ship_date = as.Date(ship_date),
date_acknowledge = as.Date(date_acknowledge),
date_acknowledgement_calc = as.Date(date_acknowledgement_calc)) %>%
DT::datatable(options = list(scrollX = TRUE))
Next things to do: make md. data description.